Time-MMD: Multi-Domain Multimodal Dataset for Time Series Analysis
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2024
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| _version_ | 1866915118851293184 |
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| author | Liu, Haoxin Xu, Shangqing Zhao, Zhiyuan Kong, Lingkai Kamarthi, Harshavardhan Sasanur, Aditya B. Sharma, Megha Cui, Jiaming Wen, Qingsong Zhang, Chao Prakash, B. Aditya |
| author_facet | Liu, Haoxin Xu, Shangqing Zhao, Zhiyuan Kong, Lingkai Kamarthi, Harshavardhan Sasanur, Aditya B. Sharma, Megha Cui, Jiaming Wen, Qingsong Zhang, Chao Prakash, B. Aditya |
| contents | Time series data are ubiquitous across a wide range of real-world domains. While real-world time series analysis (TSA) requires human experts to integrate numerical series data with multimodal domain-specific knowledge, most existing TSA models rely solely on numerical data, overlooking the significance of information beyond numerical series. This oversight is due to the untapped potential of textual series data and the absence of a comprehensive, high-quality multimodal dataset. To overcome this obstacle, we introduce Time-MMD, the first multi-domain, multimodal time series dataset covering 9 primary data domains. Time-MMD ensures fine-grained modality alignment, eliminates data contamination, and provides high usability. Additionally, we develop MM-TSFlib, the first-cut multimodal time-series forecasting (TSF) library, seamlessly pipelining multimodal TSF evaluations based on Time-MMD for in-depth analyses. Extensive experiments conducted on Time-MMD through MM-TSFlib demonstrate significant performance enhancements by extending unimodal TSF to multimodality, evidenced by over 15% mean squared error reduction in general, and up to 40% in domains with rich textual data. More importantly, our datasets and library revolutionize broader applications, impacts, research topics to advance TSA. The dataset is available at https://github.com/AdityaLab/Time-MMD. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_08627 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Time-MMD: Multi-Domain Multimodal Dataset for Time Series Analysis Liu, Haoxin Xu, Shangqing Zhao, Zhiyuan Kong, Lingkai Kamarthi, Harshavardhan Sasanur, Aditya B. Sharma, Megha Cui, Jiaming Wen, Qingsong Zhang, Chao Prakash, B. Aditya Machine Learning Computation and Language Time series data are ubiquitous across a wide range of real-world domains. While real-world time series analysis (TSA) requires human experts to integrate numerical series data with multimodal domain-specific knowledge, most existing TSA models rely solely on numerical data, overlooking the significance of information beyond numerical series. This oversight is due to the untapped potential of textual series data and the absence of a comprehensive, high-quality multimodal dataset. To overcome this obstacle, we introduce Time-MMD, the first multi-domain, multimodal time series dataset covering 9 primary data domains. Time-MMD ensures fine-grained modality alignment, eliminates data contamination, and provides high usability. Additionally, we develop MM-TSFlib, the first-cut multimodal time-series forecasting (TSF) library, seamlessly pipelining multimodal TSF evaluations based on Time-MMD for in-depth analyses. Extensive experiments conducted on Time-MMD through MM-TSFlib demonstrate significant performance enhancements by extending unimodal TSF to multimodality, evidenced by over 15% mean squared error reduction in general, and up to 40% in domains with rich textual data. More importantly, our datasets and library revolutionize broader applications, impacts, research topics to advance TSA. The dataset is available at https://github.com/AdityaLab/Time-MMD. |
| title | Time-MMD: Multi-Domain Multimodal Dataset for Time Series Analysis |
| topic | Machine Learning Computation and Language |
| url | https://arxiv.org/abs/2406.08627 |